AIMC Topic: Deep Learning

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Improving WRF-Chem PM predictions by combining data assimilation and deep-learning-based bias correction.

Environment international
In numerical model simulations, data assimilation (DA) on the initial conditions and bias correction (BC) of model outputs have been proven to be promising approaches to improving PM (particulate matter with an aerodynamic equivalent diameter of ≤ 2....

A fully value distributional deep reinforcement learning framework for multi-agent cooperation.

Neural networks : the official journal of the International Neural Network Society
Distributional Reinforcement Learning (RL) extends beyond estimating the expected value of future returns by modeling its entire distribution, offering greater expressiveness and capturing deeper insights of the value function. To leverage this advan...

Online monitoring of Haematococcus lacustris cell cycle using machine and deep learning techniques.

Bioresource technology
Optimal control and process optimization of astaxanthin production from Haematococcuslacustris is directly linked to its complex cell cycle ranging from vegetative green cells to astaxanthin-rich cysts. This study developed an automated online monito...

Utilizing InVEST ecosystem services model combined with deep learning and fallback bargaining for effective sediment retention in Northern Iran.

Environmental science and pollution research international
This study aimed to integrate game theory and deep learning algorithms with the InVEST Ecosystem Services Model (IESM) for Sediment Retention (SR) modeling in the Kasilian watershed, Iran. The Kasilian watershed is characterized by multiple sub-water...

Domain Adversarial Convolutional Neural Network Improves the Accuracy and Generalizability of Wearable Sleep Assessment Technology.

Sensors (Basel, Switzerland)
Wearable accelerometers are widely used as an ecologically valid and scalable solution for long-term at-home sleep monitoring in both clinical research and care. In this study, we applied a deep learning domain adversarial convolutional neural networ...

Automated identification of Chagas disease vectors using AlexNet pre-trained convolutional neural networks.

Medical and veterinary entomology
The 158 bug species that make up the subfamily Triatominae are the potential vectors of Trypanosoma cruzi, the etiological agent of Chagas disease. Despite recent progress in developing a picture-based automated system for identification of triatomin...

Comparison between two artificial intelligence models to discriminate cancerous cell nuclei based on confocal fluorescence imaging in hepatocellular carcinoma.

Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver
BACKGROUND: Hepatocellular carcinoma (HCC) exhibits an exceptional intratumoral heterogeneity that might influence diagnosis and outcome. Advances in digital microscopy and artificial intelligence (AI) may improve the HCC identification of liver canc...

Deep learning-based prediction of tumor aggressiveness in RCC using multiparametric MRI: a pilot study.

International urology and nephrology
OBJECTIVE: To investigate the value of multiparametric magnetic resonance imaging (MRI) as a non-invasive method to predict the aggressiveness of renal cell carcinoma (RCC) by developing a convolutional neural network (CNN) model and fusing it with c...

Enhancing space-time video super-resolution via spatial-temporal feature interaction.

Neural networks : the official journal of the International Neural Network Society
The target of space-time video super-resolution (STVSR) is to increase both the frame rate (also referred to as the temporal resolution) and the spatial resolution of a given video. Recent approaches solve STVSR using end-to-end deep neural networks....